Use of the Impella device in ambulatory heart failure and pre-heart transplant patients - one medical center’s experience
Bibliographic record
Abstract
Background and objective: The prevalence of heart failure (HF) in the US has increased over several years. The American Heart Association (AHA) documents that HF has a prevalence of 6 million cases among Americans aged 20 years and older. HF is a complex syndrome that results from the structural or functional impairment of ventricular filling or ejection of blood, leading to symptoms including poor exercise tolerance, shortness of breath, and signs of HF such as edema and rales. The aim of the study was to add to the knowledge base from one medical center's experience using Impella device in ambulatory heart failure and pre-heart transplant patients.Results: The axillary Impella enables HF patients to ambulate inside the CICU while waiting for destination therapy. RNs in our medical center's CICU can take care of these patients effectively and competently because of extensive prior experiences with the AxIABP. Nursing staff have adapted existing nursing procedures, protocols, and lessons learned from experiences with the AxIABP to manage this new patient population.Discussion: Impella is one treatment option in advanced HF and pre-heart transplant patients. The development of an alternative insertion technique that allows patients to ambulate instead of being on bedrest continues to evolve. Our medical center’s experiences with taking care of ambulatory HF and pre-heart transplant Impella patients have shown that this is a safe and effective treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".